Advances in Automatic Differentiation

This collection covers advances in automatic differentiation theory and practice. Computer scientists and mathematicians will learn about recent developments in automatic differentiation theory as well as mechanisms for the construction of robust and powerful automatic differentiation tools. Computa...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Bischof, Christian H. (Editor ), Bücker, H. Martin (Editor ), Hovland, Paul (Editor ), Naumann, Uwe (Editor ), Utke, Jean (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Lecture Notes in Computational Science and Engineering, 64
Temas:
Mathematics-Data processing.
Mathematical optimization.
Computer science.
Electrical engineering.
Computer science-Mathematics.
Computational Science and Engineering.
Optimization.
Theory of Computation.
Computational Mathematics and Numerical Analysis.
Electrical and Electronic Engineering.
Mathematics of Computing.
Acceso en línea:Texto Completo
Descripción
Sumario:This collection covers advances in automatic differentiation theory and practice. Computer scientists and mathematicians will learn about recent developments in automatic differentiation theory as well as mechanisms for the construction of robust and powerful automatic differentiation tools. Computational scientists and engineers will benefit from the discussion of various applications, which provide insight into effective strategies for using automatic differentiation for inverse problems and design optimization.
Descripción Física:XVIII, 368 p. 111 illus. online resource.
ISBN:9783540689423
ISSN:2197-7100 ;

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